More than 300 years ago, Isaac Newton created a mathematical model of the solar system that predicted the existence of a yet unknown planet: Neptune. Today, driven by the digital revolution, modern scientists are creating complex models of society itself to shed light on topics as far-ranging as epidemic outbreaks and economic growth. But how do these scientists gather and interpret their data? How accurate are their models? Can we trust the numbers? With a rare background in physics, economics and sociology, the author is able to present an insider’s view of the strengths, weaknesses and dangers of transforming our lives into numbers. After reading this book, you’ll understand how different numerical models work and how they are used in practice. The author begins by exploring several simple, easy-to-understand models that form the basis for more complex simulations. What follows is an exploration of the myriad ways that models have come to describe and define our world, from epidemiology and climate change to urban planning and the world chess championship. Highly engaging and nontechnical, this book will appeal to any readers interested in understanding the links between data and society and how our lives are being increasingly captured in numbers.

This handbook offers a unique approach to the question: How do scholars write the future of global politics? Written in futur antérieur style, around the 200-year anniversary of the birth of International Relations (IR) as an academic discipline, the contributions engage in world-building and imagine different futures of IR. Set in a multiverse, 23 chapters draw on a range of possible themes and imaginaries, for instance post-pandemic conditions, the Anthropocene, and not least academic practices and the role of researchers. A concluding chapter anchors these explorations in contemporary discussions. The book mirrors the format and style of existing handbooks, combining outlines and discussions of theories, structures, processes, and core issues in IR with an academic science fiction account of how these might play out over the course of the next century. In doing so, the book challenges IR and provides alternative imaginaries, rather than predicting future conditions for all humanity. The book invites readers to reflect on how thinking about the future has become an increasingly radical, but more than ever necessary act.

'Following the data' is a now-familiar phrase in Covid-19 policy communications. Well-being data are pivotal in decisions that affect our life chances, livelihoods and quality of life. They are increasingly valuable to companies with their eyes on profit, organisations looking to make a social impact, and governments focussed on societal problems. This book follows well-being data back centuries, showing they have long been used to track the health and wealth of society. It questions assumptions that have underpinned over 200 years of social science, statistical and policy work. Understanding Well-being Data is a readable, introductory book with real-life examples. Understanding the contexts of data and decision-making are critical for policy, practice and research that aims to do good, or at least avoid harm. Through its comprehensive survey and critical lens, this book provides tools to promote better understanding of the power and potential of well-being data for society, and the limits of their application.

Calculus for the Life Sciences is an entire reimagining of the standard calculus sequence with the needs of life science students as the fundamental organizing principle. Those needs, according to the National Academy of Science, include: the mathematical concepts of change, modeling, equilibria and stability, structure of a system, interactions among components, data and measurement, visualization, and algorithms. This book addresses, in a deep and significant way, every concept on that list. The book begins with a primer on modeling in the biological realm and biological modeling is the theme and frame for the entire book. The authors build models of bacterial growth, light penetration through a column of water, and dynamics of a colony of mold in the first few pages. In each case there is actual data that needs fitting. In the case of the mold colony that data is a set of photographs of the colony growing on a ruled sheet of graph paper and the students need to make their own approximations. Fundamental questions about the nature of mathematical modeling—trying to approximate a real-world phenomenon with an equation—are all laid out for the students to wrestle with. The authors have produced a beautifully written introduction to the uses of mathematics in the life sciences. The exposition is crystalline, the problems are overwhelmingly from biology and interesting and rich, and the emphasis on modeling is pervasive. An instructor's manual for this title is available electronically to those instructors who have adopted the textbook for classroom use. Please send email to [email protected] for more information. Online question content and interactive step-by-step tutorials are available for this title in WebAssign. WebAssign is a leading provider of online instructional tools for both faculty and students.

A Mezze of Mathematical Methods is Volume 1 of Science by Simulation. It is a recipe book of mathematical models that can be enlivened by the transmutation of equations into computer code. In this volume, the examples chosen are an eclectic mix of systems and stories rooted in common experience, rather than those normally associated with constrained courses on Physics, Chemistry or Biology which are taught in isolation and susceptible to going out of date in a few years. Rather than a 'what' of Science, this book is aimed at the 'how', readily applied to projects by students and professionals. Written in a friendly style based upon the author's expertise in teaching and pedagogy, this mathematically rigorous book is designed for readers to follow arguments step-by-step with stand-alone chapters which can be read independently. This approach will provide a tangible and readily accessible context for the development of a wide range of interconnected mathematical ideas and computing methods that underpin the practice of Science.

The demand for health information continues to increase, but the ability of health professionals to provide it clearly remains variable. The aim of this book is (1) to summarize and synthesize research on the selection and presentation of data pertinent to public health, and (2) to provide practical suggestions, based on this research summary and synthesis, on how scientists and other public health practitioners can better communicate data to the public, policy makers, and the press in typical real-world situations. Because communication is complex and no one approach works for all audiences, the authors emphasize how to communicate data "better" (and in some instances, contrast this with how to communicate data "worse"), rather than attempting a cookbook approach. The book contains a wealth of case studies and other examples to illustrate major points, and actual situations whenever possible. Key principles and recommendations are summarized at the end of each chapter. This book will stimulate interest among public health practitioners, scholars, and students to more seriously consider ways they can understand and improve communication about data and other types of scientific information with the public, policy makers, and the press. Improved data communication will increase the chances that evidence-based scientific findings can play a greater role in improving the public's health.

Work with data like a pro using this guide that breaks down how to organize, apply, and most importantly, understand what you are analyzing in order to become a true data ninja. From the stock market to genomics laboratories, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.